# 3D Question Answering

arXiv: https://arxiv.org/abs/2112.08359 · HTML: https://mlsanity.com/paper/2112.08359/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- Builds on 68 of its 86 references that are in the map.
- First posted on arXiv 15 Dec 2021.

## Builds on (most cited first, 30 of 68)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations](https://mlsanity.com/paper/1602.07332/) (2016)
- [Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering](https://mlsanity.com/paper/1612.00837/) (2016)
- [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015)
- [ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks](https://mlsanity.com/paper/1908.02265/) (2019)
- [LXMERT: Learning Cross-Modality Encoder Representations from Transformers](https://mlsanity.com/paper/1908.07490/) (2019)
- [Microsoft COCO Captions: Data Collection and Evaluation Server](https://mlsanity.com/paper/1504.00325/) (2015)
- [CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning](https://mlsanity.com/paper/1612.06890/) (2016)
- [DocVQA: A Dataset for VQA on Document Images](https://mlsanity.com/paper/2007.00398/) (2020)
- [Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering](https://mlsanity.com/paper/1707.07998/) (2017)
- [OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge](https://mlsanity.com/paper/1906.00067/) (2019)
- [Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments](https://mlsanity.com/paper/1711.07280/) (2017)
- [VL-BERT: Pre-training of Generic Visual-Linguistic Representations](https://mlsanity.com/paper/1908.08530/) (2019)
- [PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud](https://mlsanity.com/paper/1812.04244/) (2018)
- [VizWiz Grand Challenge: Answering Visual Questions from Blind People](https://mlsanity.com/paper/1802.08218/) (2018)
- [Deep Hough Voting for 3D Object Detection in Point Clouds](https://mlsanity.com/paper/1904.09664/) (2019)
- [From Recognition to Cognition: Visual Commonsense Reasoning](https://mlsanity.com/paper/1811.10830/) (2018)
- [TVQA: Localized, Compositional Video Question Answering](https://mlsanity.com/paper/1809.01696/) (2018)
- [ScanRefer: 3D Object Localization in RGB-D Scans using Natural Language](https://mlsanity.com/paper/1912.08830/) (2019)
- [Visual Dialog](https://mlsanity.com/paper/1611.08669/) (2016)
- [DVQA: Understanding Data Visualizations via Question Answering](https://mlsanity.com/paper/1801.08163/) (2018)
- [Don't Just Assume; Look and Answer: Overcoming Priors for Visual Question Answering](https://mlsanity.com/paper/1712.00377/) (2017)
- [Embodied Question Answering](https://mlsanity.com/paper/1711.11543/) (2017)
- [Scene Text Visual Question Answering](https://mlsanity.com/paper/1905.13648/) (2019)
- [FigureQA: An Annotated Figure Dataset for Visual Reasoning](https://mlsanity.com/paper/1710.07300/) (2017)
- [Visual7W: Grounded Question Answering in Images](https://mlsanity.com/paper/1511.03416/) (2015)

## Built on by (most cited first, 30 of 41)

- [SQA3D: Situated Question Answering in 3D Scenes](https://mlsanity.com/paper/2210.07474/) (2022)
- [NuScenes-QA: A Multi-modal Visual Question Answering Benchmark for Autonomous Driving Scenario](https://mlsanity.com/paper/2305.14836/) (2023)
- [LL3DA: Visual Interactive Instruction Tuning for Omni-3D Understanding, Reasoning, and Planning](https://mlsanity.com/paper/2311.18651/) (2023)
- [Scene-LLM: Extending Language Model for 3D Visual Understanding and Reasoning](https://mlsanity.com/paper/2403.11401/) (2024)
- [HUMANISE: Language-conditioned Human Motion Generation in 3D Scenes](https://mlsanity.com/paper/2210.09729/) (2022)
- [ShapeLLM: Universal 3D Object Understanding for Embodied Interaction](https://mlsanity.com/paper/2402.17766/) (2024)
- [SAT: Dynamic Spatial Aptitude Training for Multimodal Language Models](https://mlsanity.com/paper/2412.07755/) (2024)
- [GPT4Scene: Understand 3D Scenes from Videos with Vision-Language Models](https://mlsanity.com/paper/2501.01428/) (2025)
- [Move as You Say, Interact as You Can: Language-guided Human Motion Generation with Scene Affordance](https://mlsanity.com/paper/2403.18036/) (2024)
- [Unifying 3D Vision-Language Understanding via Promptable Queries](https://mlsanity.com/paper/2405.11442/) (2024)
- [3D Concept Learning and Reasoning from Multi-View Images](https://mlsanity.com/paper/2303.11327/) (2023)
- [Multi-modal Situated Reasoning in 3D Scenes](https://mlsanity.com/paper/2409.02389/) (2024)
- [Generating Human Motion in 3D Scenes from Text Descriptions](https://mlsanity.com/paper/2405.07784/) (2024)
- [ScanReason: Empowering 3D Visual Grounding with Reasoning Capabilities](https://mlsanity.com/paper/2407.01525/) (2024)
- [Vote2Cap-DETR++: Decoupling Localization and Describing for End-to-End 3D Dense Captioning](https://mlsanity.com/paper/2309.02999/) (2023)
- [SpatialLLM: A Compound 3D-Informed Design towards Spatially-Intelligent Large Multimodal Models](https://mlsanity.com/paper/2505.00788/) (2025)
- [Situational Awareness Matters in 3D Vision Language Reasoning](https://mlsanity.com/paper/2406.07544/) (2024)
- [Toward 3D Spatial Reasoning for Human-like Text-based Visual Question Answering](https://mlsanity.com/paper/2209.10326/) (2022)
- [Towards Explainable 3D Grounded Visual Question Answering: A New Benchmark and Strong Baseline](https://mlsanity.com/paper/2209.12028/) (2022)
- [Unveiling the Mist over 3D Vision-Language Understanding: Object-centric Evaluation with Chain-of-Analysis](https://mlsanity.com/paper/2503.22420/) (2025)
- [SpaCE-10: A Comprehensive Benchmark for Multimodal Large Language Models in Compositional Spatial Intelligence](https://mlsanity.com/paper/2506.07966/) (2025)
- [Space3D-Bench: Spatial 3D Question Answering Benchmark](https://mlsanity.com/paper/2408.16662/) (2024)
- [3D-Aware Visual Question Answering about Parts, Poses and Occlusions](https://mlsanity.com/paper/2310.17914/) (2023)
- [Can 3D Vision-Language Models Truly Understand Natural Language?](https://mlsanity.com/paper/2403.14760/) (2024)
- [Embodied Intelligence for 3D Understanding: A Survey on 3D Scene Question Answering](https://mlsanity.com/paper/2502.00342/) (2025)
- [PerLA: Perceptive 3D Language Assistant](https://mlsanity.com/paper/2411.19774/) (2024)
- [DSPNet: Dual-vision Scene Perception for Robust 3D Question Answering](https://mlsanity.com/paper/2503.03190/) (2025)
- [Do 3D Large Language Models Really Understand 3D Spatial Relationships?](https://mlsanity.com/paper/2603.23523/) (2026)
- [Map2Thought: Explicit 3D Spatial Reasoning via Metric Cognitive Maps](https://mlsanity.com/paper/2601.11442/) (2026)
- [Improving Commonsense in Vision-Language Models via Knowledge Graph Riddles](https://mlsanity.com/paper/2211.16504/) (2022)

## Abstract (arXiv)

Visual Question Answering (VQA) has witnessed tremendous progress in recent years. However, most efforts only focus on the 2D image question answering tasks. In this paper, we present the first attempt at extending VQA to the 3D domain, which can facilitate artificial intelligence's perception of 3D real-world scenarios. Different from image based VQA, 3D Question Answering (3DQA) takes the color point cloud as input and requires both appearance and 3D geometry comprehension ability to answer the 3D-related questions. To this end, we propose a novel transformer-based 3DQA framework "3DQA-TR", which consists of two encoders for exploiting the appearance and geometry information, respectively. The multi-modal information of appearance, geometry, and the linguistic question can finally attend to each other via a 3D-Linguistic Bert to predict the target answers. To verify the effectiveness of our proposed 3DQA framework, we further develop the first 3DQA dataset "ScanQA", which builds on the ScanNet dataset and contains ~6K questions, ~30K answers for 806 scenes. Extensive experiments on this dataset demonstrate the obvious superiority of our proposed 3DQA framework over existing VQA frameworks, and the effectiveness of our major designs. Our code and dataset will be made publicly available to facilitate the research in this direction.